ORLANDO — As a 140-year-old business, Pittsburg Plate Glass Industries is well supplied with a diverse array of data across its operations. With a presence in multiple countries, the company faced challenges with data literacy and data discoverability and sought to address them to scale its AI productivity goal.
“We’ve had poor discoverability, unclear data product ownership,” said PPG’s Bob Howden, senior IT manager of data and analytics, during a presentation at the Gartner Data & Analytics Summit 2026 on March 9. “We have gaps in the lineage; we have multiple sources. We have 170 sources in our database.”
Along with these problems, PPG also had a disconnected metadata system that would have made it challenging to scale or achieve its goal of accelerating its day-to-day AI initiatives or boost ROI on existing data in 2026.
To address the situation, PPG turned to data cataloging vendor Atlan.
PPG’s journey highlights a trend in enterprises seeking to deploy AI applications: without a strong data environment, success is hard. This challenge is leading enterprises to realize that not only is a good data environment needed, but that data must also include context that the AI system can feed on.
“The challenge I think we have today with AI is really the context,” Howden said in an interview. “We have a lot of data. It used to be data inaccuracy was a problem but understanding that data in a larger context and making and providing AI the ability to give solid answers is probably our biggest challenge.”
The need for better context in the data on which AI systems depend arises because the types of data AI agents use have changed.
“Now we’re looking at versions of AI that need either streaming data or unstructured data and more access to data,” said Beena Ammanath, lead at the Global Deloitte AI Institute. Structured data includes data that can be easily entered into spreadsheets. Unstructured data is data from images, written documents, and other sources. “You need unstructured data to make your AI robust. AI and generative AI need more varieties of data.”
While providing context to the data feeding the AI system is important, that is only the beginning, said Saravanan Balasubramaniam, senior vice president of AI and data delivery at Truist Financial, one of the country’s biggest bank holding companies, during an interview.
Enterprises should also ensure that structured and unstructured data align, he said. He noted, for example, that if a bank, such as Truist, gives two different answers to the same client for the same question, it can lead to legal action.
“If I have to pull in multiple documents about the customer and get an answer, then the document should be clear, clean,” Balasubramaniam said. “The identity resolution has to happen on the document level. The name cannot be William in one place and Will in another place.”
He added that without alignment between structured and unstructured data and without context or identity resolution, data quality problems will likely recur within either the AI system or the agent.
Providing an AI agent with the right data, with the correct context and synchronization within structured and unstructured data, could lead the system to understand faster than a human can.
“Their perception of the context might be easier, quicker, smarter,” said Paul Bell, global head of data trust and integrity at Entain PLC, an international sports betting and gambling company, in an interview.
